Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

作     者：Ayman A.El-Saleh Abdulraqeb Alhammadi Ibraheem Shayea Azizul Azizan Wan Haslina Hassan 

作者机构：College of EngineeringA’Sharqiyah University(ASU)Ibra400Oman Communication Systems and Networks Research LabMalaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Electronics and Communication EngineeringFaculty of Electrical and Electronics EngineeringIstanbul Technical University(ITU)Istanbul34467Turkey Advanced Informatics DepartmentRazak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第9期

页      面：4571-4587页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：The research leading to these results has received funding from The Research Council(TRC)of the Sultanate of Oman under the Block Funding Program with Agreement No.TRC/BFP/ASU/01/2019 

主　　题：Quality of experience quality of service bayesian networks minimum opinion score artificial intelligence prediction mobile broadband 

摘      要：Mobile broadband(MBB)networks are expanding rapidly to deliver higher data *** fifth-generation cellular network promises enhanced-MBB with high-speed data rates,low power connectivity,and ultralow latency video ***,existing cellular networks are unable to perform well due to high latency and low bandwidth,which degrades the performance of various *** a result,monitoring and evaluation of the performance of these network-supported services is *** network providers optimize and monitor their network performance to ensure the highest quality of service to their *** paper proposes a Bayesian model to estimate the minimum opinion score(MOS)of video streaming services for any particular cellular *** MOS is the most commonly used metric to assess the quality of *** proposed Bayesian model consists of several input data,namely,round-trip time,stalling load,and bite *** was examined and evaluated using several test data sizes with various performance *** results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural *** proposed Bayesian network obtained a remarkable overall accuracy of 90.36%and outperformed the neural network.